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Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20\%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. The MICCAI Brain Tumor Segmentation (BraTS) Challenge is a landmark community benchmark event with a successful history of 12 years of resource creation for the segmentation and analysis of adult glioma. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge, which represents the first BraTS challenge focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The BraTS-PEDs 2023 challenge focuses on benchmarking the development of volumentric segmentation algorithms for pediatric brain glioma through standardized quantitative performance evaluation metrics utilized across the BraTS 2023 cluster of challenges. Models gaining knowledge from the BraTS-PEDs multi-parametric structural MRI (mpMRI) training data will be evaluated on separate validation and unseen test mpMRI dataof high-grade pediatric glioma. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors.

The changes in the course of communication changed the world from different perspectives. Public participation on social media means the generation, diffusion, and exposure to a tremendous amount of user-generated content without supervision. This four-essay dissertation analyzes information diffusion through social media and its opportunities and challenges through management systems engineering and data analytics. First, we evaluate how information can be shared to reach maximum exposure for the case on online petitions. We use system dynamics modeling and propose policies for campaign managers to schedule the reminders they send to have the highest number of petition signatures. We find that sending reminders is more effective in the case of increasing the signature rate. In the second essay, we investigate how people build trust/ mistrust in science during an emergency. We use data analytics methods on more than 700,000 tweets containing keywords of Hydroxychloroquine and chloroquine, two candidate medicines, to prevent and cure patients infected with COVID-19. We show that people's opinions are concentrated in the case of polarity and spread out in the case of subjectivity. Also, they tend to share subjective tweets than objective ones. In the third essay, building on the same dataset as essay two, we study the changes in science communication during the coronavirus pandemic. We used topic modeling and clustered the tweets into seven different groups. Our analysis suggests that a highly scientific and health-related subject can become political in the case of an emergency. We found that the groups of medical information and research and study have fewer tweets than the political one. Fourth, we investigated fake news diffusion as one of the main challenges of user-generated content. We built a system dynamics model and analyzed the effects of competition and correction in combating fake news. We show that correction of misinformation and competition in fake news needs a high percentage of participation to be effective enough to deal with fake news. Doctor of Philosophy The prevalence of social media among people has changed information diffusion in several ways. This change caused the emergence of a variety of opportunities and challenges. We discuss instances of these in this dissertation in four main essays. In the first essay, we study online social and political campaigns. Considering the main goal of campaign managers is to gain the highest reach and signatures, we generate a model to show the effects of sending reminders after the initial announcement and its schedule on the final total number of signatures. We found that the best policy for online petition success is sending reminders when people are increasingly signing it rather than when people lose interest in it. In the second essay, we investigated how people build trust/ mistrust in scientific information in emergency cases. We used public tweets about two candidate medicines to prevent and treat patients infected with COVID-19 and analyzed them. Our results suggest that people trust and retweet the information that is based on emotions and judgments more than the one containing facts. We evaluated the science communication during the mentioned emergency by further investigating the same dataset in the third essay. We clustered all the tweets based on the words they used into seven different groups and labeled each of them. Then, we focused on three groups of medical, research and study, and political. Our analysis suggests that although the subject is a health-related scientific one, the number of tweets in the political group is greater than the other clusters. In the fourth essay, we analyzed the fake news diffusion through social media and the effects of correction and competition on it. In this context, correction means the reaction to misinformation that states its falsity or provides counter facts based on truth. We created a model and simulated it for the competition considering novelty as one influential factor of sharing. The results of this study reveal that active participation in correction and competition is needed to combat fake news effectively.

Additional file 1: Supplementary Table 1. Univariable logistic regression to determine predictive factors for selecting type of donor.

In recent years, several novel immunotherapeutic approaches were developed and investigated in patients with hepatocellular carcinoma (HCC). We designed this systematic review, to evaluate clinical efficacy of specific immunotherapy in patients with HCC, according to the guidelines of Border of Immune Tolerance Education and Research Network (BITERN) and Cochrane collaboration.We searched Medline, Scopus, CENTRAL, TRIP, DART, OpenGrey, and ProQuest through the 9th of December 2015. One author reviewed and retrieved citations from these seven databases for irrelevant and duplicate studies, and two other authors independently extracted data from the studies and rated their quality. We collated study findings and calculated a weighted treatment effect across studies using Review Manager.We found 12144 references in seven databases of which 21 controlled studies with 1885 HCC patients in different stages were included in this systematic review after the primary and secondary screenings. Overall, patients undergoing specific immunotherapy had significantly higher overall survival than those in control group (HR = 0.59; 95% CI = 0.47-0.76, P 0.0001). There was a significant difference in recurrence-free survival between patients undergoing specific immunotherapy and patients in control groups and patients in immunotherapy groups overall had less recurrence than control group (HR = 0.54; 95% CI = 0.46-0.63, P 0.00001).Results of this systematic review based on the available literature suggest that overall specific immunotherapeutic approaches could be beneficiary for the treatment of patients with HCC. This further supports the current and ongoing evaluations of specific immunotherapies in the field.